Package: dnr 0.3.5

dnr: Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family

Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <doi:10.1080/10618600.2019.1594834>.

Authors:Abhirup Mallik [aut, cre], Zack Almquist [aut]

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13 exports 0.00 score 54 dependencies 18 scripts 187 downloads

Last updated 4 years agofrom:91fcd30f8c. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:binaryPlotclustCoefengineEdgeengineEdgeNSengineVertexengineVertexNSexpdegntrianglesparamEdgeparamVertexparamVertexOnlyparamVertexOnlyGroupvdegree

Dependencies:abindarmbootcachemclicodacodetoolscpp11DEoptimRergmevaluatefansifastmapforeachglmnetgluehighrigraphiteratorsknitrlatticelifecyclelme4lpSolveAPImagrittrMASSMatrixmemoiseminqanetworknlmenloptrpillarpkgconfigpurrrrbibutilsRcppRcppEigenRdpackrlangrlerobustbaseshapesnastatnet.commonstringistringrsurvivaltibbletrustutf8vctrsxfunyaml